A Bayesian Statistical Personalized Recommendation System: Model Construction and Evaluation in Big Data Environment

Qinghe Guan

doi:10.54097/a9w60f06

Authors

Qinghe Guan

DOI:

https://doi.org/10.54097/a9w60f06

Keywords:

Bayesian statistics, Personalized recommendations, big data analytics, User modeling, Machine learning, Evaluation metrics, E-commerce, social media, Music recommendation, User engagement, Scalability, Ethical implications.

Abstract

In the era of big data, personalized recommendation systems have become indispensable for enhancing user experience and driving engagement across various platforms. This paper introduces a Bayesian statistical personalized recommendation system designed to effectively model user preferences in a big data environment. Leveraging the principles of Bayesian statistics, the system is capable of handling uncertainty and updating user profiles based on continuous feedback. The paper outlines the theoretical framework, including the derivation of Bayesian updating rules and the selection of appropriate priors and likelihood functions. A comprehensive evaluation of the system is conducted through offline and online methods, with a focus on precision, recall, and F1-score as key performance indicators. The system's performance is further illustrated through case studies in e-commerce, social media, and music streaming services. The paper concludes with a discussion on the system's scalability, performance optimization, and potential future enhancements, emphasizing the importance of ethical considerations in the development of personalized recommendation systems.

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